U.S. patent application number 12/646811 was filed with the patent office on 2010-07-01 for drowsiness detection method and apparatus thereof.
This patent application is currently assigned to INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE. Invention is credited to NING HUNG CHEN, ZU SHO CHOW, YUAN MEI HUANG, YU JEN SU, TEH HO TAO.
Application Number | 20100168591 12/646811 |
Document ID | / |
Family ID | 41809039 |
Filed Date | 2010-07-01 |
United States Patent
Application |
20100168591 |
Kind Code |
A1 |
TAO; TEH HO ; et
al. |
July 1, 2010 |
DROWSINESS DETECTION METHOD AND APPARATUS THEREOF
Abstract
The apparatus comprises an ultra-wide band module or an
electrocardiography module for gathering heartbeat signals of a
human being. By sequentially obtaining average heart-rate values of
a human being, and according to the features of the heart-rate
values over a period of time, the method is utilized to determine
whether the human being is going to a state of drowsiness.
Inventors: |
TAO; TEH HO; (HSINCHU CITY,
TW) ; SU; YU JEN; (KAOHSIUNG CITY, TW) ;
HUANG; YUAN MEI; (TAOYUAN COUNTY, TW) ; CHOW; ZU
SHO; (HSINCHU COUNTY, TW) ; CHEN; NING HUNG;
(TAIPEI CITY, TW) |
Correspondence
Address: |
WPAT, PC;INTELLECTUAL PROPERTY ATTORNEYS
2030 MAIN STREET, SUITE 1300
IRVINE
CA
92614
US
|
Assignee: |
INDUSTRIAL TECHNOLOGY RESEARCH
INSTITUTE
HSINCHU
TW
|
Family ID: |
41809039 |
Appl. No.: |
12/646811 |
Filed: |
December 23, 2009 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
61141861 |
Dec 31, 2008 |
|
|
|
Current U.S.
Class: |
600/500 ;
600/509 |
Current CPC
Class: |
A61B 5/0245 20130101;
A61B 5/18 20130101 |
Class at
Publication: |
600/500 ;
600/509 |
International
Class: |
A61B 5/024 20060101
A61B005/024; A61B 5/0402 20060101 A61B005/0402 |
Claims
1. A drowsiness detection method, comprising: detecting a plurality
of physiological feature values of an object and storing the
plurality of physiological feature values into a queue; obtaining a
plurality of specific values from the queue, wherein the plurality
of specific values comprise a first minimum value, a second minimum
value, a first maximum value, and a value of a first position; and
obtaining a plurality of difference values between the plurality of
specific values and comparing the plurality of difference values
with at least one threshold value to generate a drowsiness
detection result.
2. The method of claim 1, wherein the plurality of physiological
feature values are obtained according to peak numbers of a
plurality of sequential signals of the object.
3. The method of claim 1, wherein the plurality of physiological
feature values are a plurality of heartbeat frequencies or a
plurality of pulse frequencies.
4. The method of claim 1, wherein the first minimum value is a
first local minimum value found from the front of the queue.
5. The method of claim 1, wherein the second minimum value is a
minimum value obtained within a queue length by searching from a
position of the first minimum value of the queue.
6. The method of claim 1, where the first maximum value is a
maximum value obtained within a queue length by searching from the
position of the first minimum value of the queue.
7. The method of claim 1, wherein the plurality of difference
values comprise a first difference value between the value of the
first position and the first minimum value, and a second difference
value between the first maximum value and the first minimum
value.
8. The method of claim 7, further comprising a step of generating
an alarm message and deleting all stored values in the queue if the
first difference value is greater than or equal to a first
threshold value and the second difference value is less than or
equal to a second threshold value.
9. A drowsiness detection method, comprising: detecting a plurality
of physiological feature values of an object and storing the
plurality of physiological feature values into a queue; obtaining a
maximum value, a first minimum value, and a second minimum value in
the queue; and obtaining a plurality of difference values between
the maximum value, the first minimum value, and the second minimum
value, and comparing the plurality of difference values with a
plurality of threshold values to generate a drowsiness detection
result.
10. The method of claim 9, wherein the plurality of physiological
feature values are obtained according to peak numbers of a
plurality of sequential signals of the object.
11. The method of claim 9, wherein the plurality of physiological
feature values are a plurality of heartbeat frequencies or a
plurality of pulse frequencies.
12. The method of claim 9, wherein the first minimum value and the
second minimum value are minimum values obtained by searching from
the front of the queue to a position of the maximum value and from
the end of the queue to the position of the maximum value,
respectively.
13. The method of claim 9, wherein the plurality of difference
values comprise a first difference value and a second difference
value, wherein the first difference value is a difference value
between the maximum value and the first minimum value and a second
difference value is a difference value between the maximum value
and the second minimum value.
14. The method of claim 13, further comprising a step of generating
an alarm message and deleting all the stored values of the queue if
the first difference value is greater than or equal to a first
threshold value and the second difference value is smaller than or
equal to a second threshold value.
15. A drowsiness detection method, comprising: detecting a
plurality of physiological feature values of an object and storing
the plurality of physiological feature values in first and second
queues; obtaining a first maximum value, a first minimum value, and
a second minimum value of the first queue; obtaining a difference
value between the first maximum value and the first minimum value
and comparing the difference with a first threshold value and
obtaining a difference value between the maximum value and the
second minimum value and comparing the difference with a second
threshold value, respectively, to generate a first comparison
result; obtaining a difference value between a second maximum value
and a third minimum value of the second queue and comparing the
difference with a third threshold value to generate a second
comparison result; and generating a drowsiness detection result
according to the first and second comparison results.
16. The method of claim 15, wherein the plurality of physiological
feature values are obtained according to peak numbers of a
plurality of sequential signals of the object.
17. The method of claim 15, wherein the plurality of physiological
feature values are a plurality of heartbeat frequencies or a
plurality of pulse frequencies.
18. The method of claim 15, wherein the first maximum value is a
maximum value of the first queue.
19. The method of claim 15, wherein the first minimum value and the
second minimum value are minimum values obtained by searching from
the front of the first queue to a position of the first maximum
value and from the end of the first queue to the position of the
first maximum value, respectively.
20. The method of claim 15, wherein the second maximum value is a
maximum value of the second queue.
21. The method of claim 15, wherein the third minimum value is a
minimum value of the second queue.
22. The method of claim 15, further comprising a step of generating
a first comparison result and deleting all the stored values of the
first queue if a difference value between the first maximum value
and the first minimum value is greater than or equal to the first
threshold value while a difference value between the first maximum
value and the second minimum value is greater than or equal to the
second threshold value.
23. The method of claim 22, further comprising a step of generating
a second comparison result if a difference value between the second
maximum value and the third minimum value is less than or equal to
the third threshold value.
24. The method of claim 23, further comprising a step of generating
an alarm message when the first comparison result is generated
before the second comparison result while the period of the first
and second comparison results is less than or equal to a time
interval.
25. A drowsiness detection apparatus, comprising: a signal
detection unit configured to obtain a plurality of sequential
signals of an object during a plurality of time intervals; and an
operation module configured to convert the plurality of sequential
signals into a plurality of frequencies and to obtain mutual
relationships of the plurality of frequencies, so as to generate a
drowsiness detection result.
26. The apparatus of claim 25, further comprising a storage medium
configured to store the plurality of frequencies.
27. The apparatus of claim 25, further comprising an alarm
configured to generate a plurality of messages according to the
drowsiness detection result.
28. The apparatus of claim 25, wherein the operation module is
configured to convert peak numbers of the plurality of sequential
signals into the plurality of frequencies.
29. The apparatus of claim 25, wherein periods of the plurality of
time intervals can be the same, partially different, or totally
different.
30. The apparatus of claim 25, wherein the plurality of frequencies
are a plurality of heartbeat frequencies or a plurality of pulse
frequencies.
31. The apparatus of claim 25, wherein the plurality of frequencies
are average frequencies during the plurality of time intervals.
32. The apparatus of claim 25, wherein the signal detection unit
comprises: an ultra-wide band antenna configured to emit a
plurality of ultra-wide band signals; and a receiver configured to
receive reflected signals after the plurality of ultra-wide band
signals pass through the object and to obtain the plurality of
sequential signals during the plurality of time intervals.
33. The apparatus of claim 25, wherein the signal detection unit
comprises an electrocardiography module.
Description
TECHNICAL FIELD
[0001] The disclosure relates to a drowsiness detection method and
apparatus thereof.
BACKGROUND
[0002] While driving long distances, as drivers focus attention for
lengthy periods of time on the road and the car, a driver can
easily become very tired or even fall asleep. In the early stages
of drowsiness, the driver may fall asleep for very brief moments.
Attention lapses and reduced alertness occur for short periods
(less than 30 seconds) but the driver usually awakens with an
awareness of danger. However, the driver subsequently feels weary,
and continues to drift repeatedly in and out of consciousness until
finally falling completely asleep.
[0003] In addition, persons working under highly dangerous
conditions, e.g., analysts dealing with dangerous materials
analysis, are likely to become lethargic in a very short time in
quiet environments requiring highly focused attention. People who
become drowsy while working under such conditions cannot pay full
attention to dangers in their surroundings.
[0004] U.S. Pat. No. 7,088,250 discloses a fatigue-level estimation
apparatus to determine a fatigue level of a subject. U.S. Pat. No.
6,070,098 utilizes an observation of activities related to fatigue
and determines a level of fatigue based on a large amount of
processed data. U.S. Pat. No. 4,967,186 utilizes an IR beam to
detect the reflectivity of the eyelid for determining levels of
fatigue. However, the detected data of a drowsy subject, such as
observations of a driver's behavior or the reflectivity of the
eyelid, may exhibit similar behaviors to those of an alert subject.
Therefore, there is a need to reduce required data processing
amount and to detect drowsiness effectively, so as to meet
industrial requirements.
SUMMARY
[0005] A drowsiness detection method and apparatus thereof are
disclosed, whereby the drowsiness detection is performed according
to heartbeat frequencies during a plurality of time intervals.
[0006] One embodiment discloses a drowsiness detection method,
comprising the steps of: detecting a plurality of physiological
feature values of an object and storing the plurality of
physiological feature values in a queue; obtaining a plurality of
specific values from the queue, wherein the plurality of specific
values comprise a first minimum value, a second minimum value, a
first maximum value, and a value of a first position; and obtaining
a plurality of difference values between the plurality of specific
values and comparing the plurality of difference values with at
least one threshold value to generate a drowsiness detection
result.
[0007] Another embodiment discloses a drowsiness detection method,
comprising the steps of: detecting a plurality of physiological
feature values of an object and storing the plurality of
physiological feature values in a queue; obtaining a maximum value,
a first minimum value, and a second minimum value of the queue;
obtaining a plurality of difference values between the maximum
value, the first minimum value, and the second minimum value; and
comparing the plurality of difference values with a plurality of
threshold values to generate a drowsiness detection result.
[0008] Another embodiment discloses a drowsiness detection method,
comprising the steps of: detecting a plurality of physiological
feature values of an object and storing them to first and second
queues; obtaining a first maximum value, a first minimum value, and
a second minimum value of the first queue; obtaining a difference
value between the first maximum value and the first minimum value
and comparing the difference with a first threshold value and
obtaining a difference value between the maximum value and the
second minimum value and comparing the difference with a second
threshold value, respectively, to generate a first comparison
result; obtaining a difference value between the second maximum
value and the third minimum value of the second queue and comparing
the difference with a third threshold value to generate a second
comparison result; and generating a drowsiness detection result
according to the first and second comparison results.
[0009] Another embodiment discloses a drowsiness detection
apparatus comprising a signal detection unit and an operation
module. The signal detection unit is configured to obtain a
plurality of sequential signals of an object during a plurality of
time intervals. The operation module is configured to convert the
plurality of sequential signals into a plurality of frequencies and
to obtain mutual relationships between the plurality of
frequencies, so as to generate a drowsiness detection result.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] The accompanying drawings, which are incorporated in and
constitute a part of this specification, illustrate embodiments of
the disclosure and, together with the description, serve to explain
the principles of the invention.
[0011] FIG. 1 is a flowchart illustrating an exemplary embodiment
of the drowsiness detection method;
[0012] FIG. 2 shows the detail of step S106 in accordance with an
exemplary embodiment;
[0013] FIG. 3 illustrates a detection result in accordance with an
exemplary embodiment;
[0014] FIG. 4 shows the detail of step S106 in accordance with
another exemplary embodiment;
[0015] FIG. 5 illustrates a detection result in accordance with
another exemplary embodiment;
[0016] FIG. 6 shows the detail of step S106 in accordance with yet
another exemplary embodiment;
[0017] FIG. 7 illustrates the first condition determining process
in step S601 in accordance with one exemplary embodiment;
[0018] FIG. 8 illustrates the second condition determining process
in step S602 in accordance with one exemplary embodiment;
[0019] FIG. 9 illustrates a detection result in accordance with yet
another exemplary embodiment;
[0020] FIG. 10 illustrates a system block diagram of a drowsiness
detection apparatus in accordance with an exemplary embodiment;
and
[0021] FIG. 11 illustrates a system block diagram of a drowsiness
detection apparatus in accordance with another exemplary
embodiment.
DETAILED DESCRIPTION
[0022] FIG. 1 is a flowchart illustrating an exemplary embodiment
of the drowsiness detection method. In step S101, a drowsiness
detection procedure is activated. In step S102, heartbeat signals
during a first time interval are obtained. The heartbeat signals in
the exemplary embodiment are measured with an electrocardiogram
machine or an ultra wideband heartbeat detection antenna. In step
S103, heartbeat signals during a second time interval are obtained.
In step S104, heartbeat signals during a third time interval are
obtained, and the peak numbers of the heartbeat signals are
converted into a heartbeat frequency. The unit of measure of the
heartbeat frequency is "beats per minute." The heartbeat frequency
is a physiological feature value in the exemplary embodiment. Also,
the physiological feature value can also be a pulse frequency
derived from pulse signals. In step S105, the latest heartbeat
frequency is stored in a heartbeat queue. In step S106, a
determining procedure is performed. Step S107 determines whether
the drowsiness detection procedure is ended. If No, the process
returns to step S103, while if YES, then the procedure in Step S108
is ended. In the exemplary embodiment, the first time interval is
substantially equal to 20 seconds, the second time interval is
substantially equal to 10 seconds, and the third time interval is
substantially equal to 30 seconds. The latest heartbeat signals
gathered during the third time interval (about 30 seconds) comprise
heartbeat signals gathered during the second time interval (about
10 seconds) and heartbeat signals gathered within a duration of
about 20 seconds prior to the second time interval. In addition,
the length of the queue in the exemplary embodiment can be designed
according to the gathered frequency for obtaining the heartbeat
frequency. The queue in the exemplary embodiment has 20 storage
positions, and each of the heartbeat frequencies most recently
obtained is stored in the front of the queue, i.e., a first
position of the queue. If 20 positions are occupied with the stored
heartbeat frequencies, then when a next heartbeat frequency is
obtained, the stored value in the first position of the queue is
deleted, each of the values stored at other positions in the queue
is moved one position before its original position, and the
heartbeat frequency most recently obtained is stored in the end of
the queue. The above-mentioned process can be implemented using
indexes of the queue.
[0023] FIG. 2 shows the details of step S106 in accordance with an
exemplary embodiment. In step S105, the latest heartbeat frequency
is stored in the heartbeat queue. In step S201, a heartbeat
frequency HRHead (in the front of the queue), and the value HRDown
(the first local smallest value in the queue to appear which is
smaller than the previous value and not smaller than the following
value in the queue, as the queue is searched from the front to the
end) are obtained. A method for searching HRDown is to search from
the front of the heartbeat queue to the end of the heartbeat queue
and compare every two heartbeat frequencies in the queue until the
heartbeat frequency stops decreasing. Step S202 determines whether
the difference value between HRHead and HRDown is greater than a
drop threshold value. If No, then it is determined that the object
is not going to a state of drowsiness (step S206). If YES, then in
step S203, a maximum value (StbMax) and a minimum value (StbMin) in
a queue length (StbLen) are searched from the storage position of
the heartbeat frequency HRDown. Step S204 determines whether the
difference value between StbMax and HRDown is less than a condition
threshold value. If No, then the object is not going to a state of
drowsiness (step S206). If YES, then the object is going to a state
of drowsiness and a system issues a drowse alert while deleting all
stored values in the heartbeat queue (step S205). In step S207, the
determining procedure is ended. In the exemplary embodiment, the
drop threshold value=DownTh.times.HRHead, wherein the value of
DownTh is substantially equal to 0.1. In the exemplary embodiment,
the length of the StbLen is substantially equal to 6 positions and
the condition threshold value=StbTh.times.(HRHead-HRDown), wherein
the value of StbTh is substantially equal to 0.4.
[0024] FIG. 3 illustrates a detection result in accordance with an
exemplary embodiment. In FIG. 3, the system issues a drowse alert
after obtaining a heartbeat frequency 31. A section 32 is a
sleeping section obtained by artificially reading a brain wave of
the object.
[0025] FIG. 4 shows the details of step S106 in accordance with
another exemplary embodiment. In step S105, the latest heartbeat
frequency is stored in the heartbeat queue. In step S401, a maximum
heartbeat frequency (HRMax) in the queue is obtained by searching
from the end of the heartbeat queue to the front of the heartbeat
queue. In step S402, minimum values MinH and MinT are obtained by
searching from the front of the queue to the location of the
heartbeat frequency HRMax and from the end of the queue to the
location of the heartbeat frequency HRMax, respectively. Step S403
determines whether the difference in value between HRMax and MinH
is greater than or equal to a rising threshold value and the
difference value between HRMax and MinT is greater than or equal to
a falling threshold value. If YES, then the object is going to a
state of drowsiness and the system issues a drowse alert and
deletes all stored values in the heartbeat queue (step S404). If
NO, then the object is not going to a state of drowsiness (step
S405). In step S406, the determining procedure is ended. In the
exemplary embodiment, the rising threshold
value=UpRatio.times.MinH, wherein the value of the UpRatio is
substantially equal to 0.1. In the exemplary embodiment, the
falling threshold value=DownRatio.times.HRMax, wherein the value of
the DownRatio is substantially equal to 0.1.
[0026] FIG. 5 illustrates a detection result in accordance with
another exemplary embodiment. In FIG. 5, the system issues a drowse
alert after obtaining a heartbeat frequency 51. A section 52 is a
sleeping section obtained by artificially reading a brain wave of
the object.
[0027] FIG. 6 shows the details of step S106 in accordance with yet
another exemplary embodiment. In step S105, the latest heartbeat
frequency is stored in the heartbeat queue. In step S601, a first
condition determining procedure is performed. In step S601, a
second condition determining procedure is performed. Step S603
determines whether a first condition exists before a second
condition exists and whether the time interval between the
existence of the first condition and the existence of the second
condition is not greater than an interval threshold value (the
interval threshold value is substantially equal to 3 minutes in the
exemplary embodiment). If YES, then it is determined that the
object is going to a state of drowsiness and the system issues a
drowse alert. In step S605, the determining procedure is ended.
[0028] FIG. 7 illustrates the first condition determining procedure
in step S601 in accordance with an exemplary embodiment. In step
S105, the latest heartbeat frequency is stored in the heartbeat
queue. In step S701, a maximum heartbeat frequency (HRMax) in the
queue is obtained by searching from the end of the heartbeat queue
to the front of the heartbeat queue. In step S702, minimum values
MinH and MinT are obtained by searching from the front of the queue
to the location of the heartbeat frequency HRMax and from the end
of the queue to the location of the heartbeat frequency HRMax,
respectively. Step S703 determines whether the difference value
between HRMax and MinH is greater than or equal to a rising
threshold value and the difference value between HRMax and MinT is
greater than or equal to a falling threshold value. If YES, then
the first condition determination exists and all stored values in
the heartbeat queue are deleted (step S704). If NO, then the first
condition determination does not exist (step S705). In step S706,
the determining result is reported. In the exemplary embodiment,
the rising threshold value=UpRatio.times.MinH, wherein the value of
the UpRatio is substantially equal to 0.1. In the exemplary
embodiment, the falling threshold value=DownRatio.times.HRMax,
wherein the value of the DownRatio is substantially equal to
0.1.
[0029] FIG. 8 illustrates the second condition determining
procedure in step S602 in accordance with one exemplary embodiment.
In step 801, the latest heartbeat frequency is stored in a
heartbeat queue (P2Len). In step S802, a maximum value (HRMax) and
a minimum value (HRMin) in the queue P2Len are obtained. Step S803
determines whether the difference value between HRMax and HRMin is
less than or equal to a range threshold value (HRRange). If YES,
then the second condition determination exists (step S804). If NO,
then the second condition determination does not exist (step S805).
In step S806, the determining result is reported. The queue P2Len
is a queue with 6 stored positions in the exemplary embodiment.
HRRange is substantially equal to 2 beats per minute.
[0030] FIG. 9 illustrates a detection result in accordance with yet
another exemplary embodiment. In FIG. 9, the system issues a drowse
alert after obtaining a heartbeat frequency 91. A section 92 is a
sleeping section obtained by artificially reading a brain wave of
the object.
[0031] In order to enable persons skilled in the art to practice
the invention in accordance with an exemplary embodiment, an
apparatus of an exemplary embodiment is provided in accordance with
the above-mentioned drowsiness detection method.
[0032] FIG. 10 illustrates a system block diagram of a drowsiness
detection apparatus in accordance with an exemplary embodiment. An
ultra-wide band signal is emitted to a human body (not shown) by an
ultra-wide band antenna 101. A receiver 102 is utilized to receive
reflected heartbeat signals after the ultra-wide band signal passes
through a human being and is utilized to obtain sequential signals
of time intervals during a plurality of time intervals. Periods of
the plurality of time intervals can be the same, partially
different, or totally different. An operation module 103 is
utilized to convert the sequential signals into a heartbeat
frequency according to the peak numbers of every set of sequential
signals and stores the heartbeat frequency in a storage medium 104.
The heartbeat frequency is a physiological feature value in the
exemplary embodiment. The physiological feature value can also be a
pulse frequency derived from pulse signals. The operation module
103 is utilized to process the relationship of the heartbeat
frequency stored in the storage medium 104 and to determine whether
an object is going to a state of drowsiness. An alarm 105 is
utilized to produce an alarm message according to the output result
of the operation module 103.
[0033] FIG. 11 illustrates a system block diagram of a drowsiness
detection apparatus in accordance with another exemplary
embodiment. A signal detection unit 111 is utilized to obtain
sequential signals of time intervals during a plurality of time
intervals. Periods of the plurality of time intervals can be the
same, partially different, or totally different. An operation
module 112 converts the sequential signals into a heartbeat
frequency according to the peak numbers of every set of sequential
signals and stores the heartbeat frequency in a storage medium 113.
The heartbeat frequency is a physiological feature value in the
exemplary embodiment. The physiological feature value can also be a
pulse frequency derived from pulse signals. The operation module
112 is utilized to process the relationship of the heartbeat
frequency stored in the storage medium 113 and to determine whether
an object is going to a state of drowsiness. An alarm 114 is
utilized to produce an alarm message according to the output result
of the operation module 112.
[0034] The above-described exemplary embodiments are intended to be
illustrative only. Those skilled in the art may devise numerous
alternative embodiments without departing from the scope of the
following claims.
* * * * *